Orientadores: Carlos Roberto de Souza Filho, Clive Matthew Martin Oppenheimer / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Geociências / Made available in DSpace on 2018-08-22T23:47:32Z (GMT). No. of bitstreams: 1 Murphy_SamuelWilliam_D.pdf: 5456096 bytes, checksum: 83ba771a9ac314dbabebef173f7abdf1 (MD5) Previous issue date: 2013 / Resumo: O monitoramento de vulcões é necessário para a mitigação do perigo que apresentam à sociedade. Esse monitoramento pode ser feito em uma escala global através de satélites. Foi com essa finalidade que este doutorado visou desenvolver metodologias para o monitoramento das atividades termais dos vulcões observados do espaço. Chegou-se a isso através do uso de uma variedade de sensores infravermelhos orbitais. Foi utilizado o sensor Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), que oferece imagens de moderada a alta resolução espacial (30 - 90 m) no infravermelho das ondas curtas (SWIR) e infravermelho termal (TIR), a fim de caracterizar o tamanho e a intensidade das anomalias termais. As bandas do TIR do ASTER conseguiram detectar anomalias sutis, o que permitiu a observação de novos precursores termais antes das erupções. O Moderate Resolution Imaging Spectroradiometer (MODIS) oferece imagens de alta resolução temporal (i.e. cobertura global diária), por isto foi utilizado para investigar atividade termal através do tempo. A análise de wavelets foi utilizada para quantificar os períodos de oscilação de tal atividade. Hyperion é um espectroradiômetro imageador e foi utilizado para demonstrar uma nova metodologia para calcular fluxo radiante de alvos termalmente heterogêneos (i.e. vulcões). Essas metodologias utilizam as melhores resoluções de cada um dos sensors: espacial (ASTER), temporal (MODIS), espectral (Hyperion) pode ser automaticamente executadas em escala global. Essa tese visa, portanto apresentar uma fundação sólida onde futuros sistemas de monitoramento de vulcões podem ser baseados / Abstract: Volcanoes need to be monitored to mitigate the risk that they pose to society. This can be done on a global scale using satellite sensors. The goal of this doctorate was to develop methods for the monitoring of volcanic thermal activity from space. This was achieved through the use of a variety of orbital infrared sensors. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which offers moderate to high spatial resolution imagery (30 - 90 m) in the short-wave infrared (SWIR) and thermal infrared (TIR), was used to characterize the size and intensity of thermal anomalies. Its TIR channels were capable of detecting subtle thermal anomalies. This permitted the observation of new thermal precursors to eruptive events. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers high temporal resolution imagery (i.e. daily global coverage). It was therefore used to investigate thermal activity through time. Wavelet analysis was used to quantify the time period over which such activity oscillates. Hyperion is an orbiting imaging spectrometer. It was used to demonstrate a new method for calculating radiant flux from thermally heterogeneous targets (i.e. such as volcanoes). These methods utilized the strengths of each sensor system, i.e. high spatial, temporal or spectral resolution. They all improve the ability to detect and/or quantify thermal anomalies from space and can be executed in an automated global basis. This thesis therefore presents a solid foundation on which the next generation global volcano monitoring system can be based / Doutorado / Geologia e Recursos Naturais / Doutor em Ciências

This thesis reports a summary of the PhD programme for the assessment of person‘s emotional anxiety using Electro-optical technology. The thesis focuses mainly on the understanding of fundamental properties of physiological responses to emotional anxiety and how they can be captured by using Electro-optical (EO) imaging methods such as hyperspectral imaging (HSI) and thermal imaging (TI) techniques. The thesis summarises three main areas of work that have been undertaken by the author in the programme: (a) Experimental set up including HSI system and data acquisition software design and implementation, (b) fundamental understanding of physiological responses to emotional anxiety from the EO perspective and (c) the development of a novel remote sensing technique for the assessment of emotions without the requirement of base line information. One of our main results is to provide evidence to prove that the mean temperature in the periorbital region remains the same within 0.2°C during emotional anxiety. Furthermore, we have shown that it is the high temperature pixels within the periorbital, which increases in numbers by a huge amount after 2 minutes of the onset of anxiety. We have also developed techniques to allow the assessment anxiety without the need of base line information. The method has been tested using a sample size of about 40 subjects, and achieved promising result. Technologies for the remote sensing of heart beat rate has been in great demand, this study also involves the development of heart beat detection using TI system. Moreover, we have also attempted for the first time to sense glucose concentration from the blood sample in-vivo using HSI technique remotely. / ©Cranfield University

Habitat loss is widely acknowledged as the leading cause of extinctions and is occurring at an alarming rate and affecting biodiversity globally. I measured the rate of habitat loss using satellite-based land cover change data. First, I modelled the potential suitable habitat of the Marbled Murrelet on Vancouver Island using two techniques and compared those results to in-situ field measurements. Both modelling techniques predicted declines in suitable habitat between the years, although one technique was better at predicting suitable habitat. I also compared rates of habitat loss in areas of high species endangerment to those with lower endangerment over a 15-year period in three Canadian ecozones. In two ecozones, rates of habitat loss were higher in sites of high species at risk richness than those with low richness. These results underscore the importance of using remote sensing data as a monitoring tool critical habitat for species at risk in Canada.

Coccidioidomycosis, or Valley Fever, is an infectious disease caused by inhalation of soil-dwelling fungus Coccidioides posadasii spores in the Lower Sonoran Life Zone (LSLZ) in Arizona. In the context of climate change, the habitat of environmentally-mediated infectious diseases, such as Valley Fever, are expected to change. Connections have been drawn between climate and Valley Fever infection. The operational scale of the organism is still unknown. Here, we use climatic variables, including precipitation, soil moisture, and temperature. We use PRISM precipitation and temperature data, and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) as a measure of soil moisture for the entire state of Arizona, divided into 126 primary care areas (PCA). These data are analyzed and regressed with Valley Fever incidence to determine the effects of climatic variability on disease distribution and timing. This study confirms that Valley Fever occurrence is clustered in the LSLZ. Seasonal Valley Fever outbreak was found to be variable year-to-year based on climatic variability. The inconclusive regression analyses indicate that the operational scale of Coccidioides is smaller than the PCA region. All variables are related to Valley Fever infection, but one variable was not found to hold more predictive power than others.

<p> The primary goal of this research is to advance methods for deriving fine-grained, scalable, wildland fuels attributes in 3-dimensions using terrestrial and airborne laser scanning technology. It is fundamentally a remote sensing research endeavor applied to the problem of fuels characterization. Advancements in laser scanning are beginning to have significant impacts on a range of modeling frameworks in fire research, especially those utilizing 3-dimensional data and benefiting from efficient data scaling. The pairing of laser scanning and fire modeling is enabling advances in understanding how fuels variability modulates fire behavior and effects. </p><p> This dissertation details the development of methods and techniques to characterize and quantify surface fuelbeds using both terrestrial and airborne laser scanning. The primary study site is Eglin Airforce Base, Florida, USA, which provides a range of fuel types and conditions in a fire-adapted landscape along with the multi-disciplinary expertise, logistical support, and prescribed fire necessary for detailed characterization of fire as a physical process. Chapter 1 provides a research overview and discusses the state of fuels science and the related needs for highly resolved fuels data in the southeastern United States. Chapter 2, describes the use of terrestrial laser scanning for sampling fuels at multiple scales and provides analysis of the spatial accuracy of fuelbed models in 3-D. Chapter 3 describes the development of a voxel-based occupied volume method for predicting fuel mass. Results are used to inform prediction of landscape-scale fuel load using airborne laser scanning metrics as well as to predict post-fire fuel consumption. Chapter 4 introduces a novel fuel simulation approach which produces spatially explicit, statistically-defensible estimates of fuel properties and demonstrates a pathway for resampling observed data. This method also can be directly compared to terrestrial laser scanning data to assess how energy interception of the laser pulse affects characterization of the fuelbed. Chapter 5 discusses the contribution of this work to fire science and describes ongoing and future research derived from this work. Chapters 2 and 4 have been published in International Journal of Wildland Fire and Canadian Journal of Remote Sensing, respectively, and Chapter 3 is in preparation for publication.</p><p>

The upcoming launch of the German hyperspectral satellite: Environmental Mapping and Analysis Program (EnMAP) will provide potential for producing improved remotely sensed maps in areas of exposed bedrock in advance of Arctic geology programs. This study investigates the usefulness of this moderate resolution (30m) sensor for predictive lithological mapping using simulated imagery to classify a map area dominated by mafic and felsic volcanics and minor sedimentary and volcaniclastic rocks in the Hope Bay Greenstone Belt of the Northwest Territories. The assessment also included the classification of high resolution and fidelity airborne (ProSpecTIR–SPECIM Dual sensor) hyperspectral imagery for comparison to understand the impact of combined lower signal-to-noise ratio (SNR), and spectral and spatial resolutions associated with EnMap. The performance of both sensors was assessed through statistical analysis of the classification results based on partial unmixing of the data as well as common geological band indices. The results obtained from these analyses were compared to a detailed published geological map of the study area. Both sensors, the airborne ProSpecTIR–SPECIM and spaceborne EnMap, provided good results however despite the simulated EnMap data’s lower resolution and SNR, the results showed it to have greater statistical accuracy and to be visually representative of the mapped geology. The results demonstrated that EnMap satellite hyperspectral technology is an effective tool for mapping lithology in the Canadian North. The discrimination of rock compositions was successful when their occurrences were spatially large and abundant; however, it was identified that spectral similarity between unit classes and spectral variability within classes are critical factors in mapping lithology. / Graduate

This dissertation investigates the feasibility of creating real-time three-dimensional images, using data obtained from satellites. The aim is to enhance satellite imaging applications, by utilizing the normal 3-D visual perceptions of humans. A study is made of the different methods developed to create the illusion of seeing a three-dimensional object from essentially two-dimensional images. 3-D display devices based on the principles of human stereoscopic vision do exist. Other 3-D display techniques include holograms and volumetric displays. Satellite images are used in a wide range of applications, from urban planning, to earth surveillance, and even weather prediction. In the past, satellite imaging was the express domain of experts, trained in the analysis and interpretation of satellite images. However, in recent years, the acquisition and analysis of satellite images have been greatly facilitated by the growing number of commercial satellites in our skies, as well as readily available software packages. Satellite images are available in many types of image formats, and can represent a large variety of information about an area. The model developed for this dissertation (the ACSI-3D model) proposes a method for the conversion of satellite images to suitable input for a stereoscopic 3-D display device. The model covers the process from initial image acquisition to the final display. It consists of four basic phases: Image Acquisition, Stereopsis, Sequencing and Synchronization, and Display. The “Stereoscopic Image Pair Creator” prototype was developed to test parts of this model. / Ehlers, E.M., Prof.

Satellite imagery such as Landsat has been in use for decades for many landscape and regional scale mapping applications, but has been too coarse for use in detailed forest inventories where stand level structural and compositional information is desired. Recently available high spatial resolution satellite imagery may be well suited to mapping fine-scale components of ecosystems, however, this remains an area of ongoing research. The first goal of this thesis was to assess the capacity of high spatial resolution satellite imagery to detect the variability in late seral coastal temperate rainforests in British Columbia, Canada. Using an object-based classifier, two hierarchical classification schemes are evaluated: a broad classification based on structural (successional) stage and a finer classification of late seral vegetation associations. The finer-scale classification also incorporates ancillary landscape positional variables (elevation and potential soil moisture) derived from Light Detection and Ranging (LiDAR) data, and the relative contribution of spectral, textural and landscape positional data for this classification is determined. Results indicate that late seral forests can be well distinguished from younger forests using QuickBird spectral and textural data. However, discrimination among late seral forest associations is challenging, especially in the absence of landscape positional variables. Classification accuracies were particularly low for rare forest associations. Given this finding, the objective of the third chapter was to explicitly examine the caveats of using high spatial resolution imagery to map rare classes. Classification accuracy is assessed in several different ways in order to examine the impact on perceived map accuracy. In addition, the effects on habitat extent and configuration resulting from post-classification implementation of a minimum mapping unit are examined. Results indicate that classification accuracies may vary considerably depending on the assessment technique used. Specifically, ignoring the presence of fine-scale heterogeneity in a classification during accuracy assessment falsely lowered the accuracy estimates. Further, post-classification smoothing had a large effect on the spatial pattern of rare classes. These findings suggest that routinely used image classification and assessment techniques can greatly impact mapping of rare classes. / Forestry, Faculty of / Graduate

Global modeling of gross primary production (GPP) is a critical component of climate change research. On local scales, GPP can be assessed from measuring CO₂ exchange above the plant canopy using tower-based eddy covariance (EC) systems. The limited footprint inherent to this method however, restricts observations to relatively few discrete areas making continuous predictions of global CO₂ fluxes difficult. Recently, the advent of high resolution optical remote sensing devices has offered new possibilities to address some of the scaling issues related to GPP using remote sensing. One key component for inferring GPP spectrally is the efficiency (ε) with which plants can use absorbed photosynthetically active radiation to produce biomass. While recent years have seen progress in measuring ε using the photochemical reflectance index (PRI), little is known about the temporal and spatial requirements for up-scaling these findings continuously throughout the landscape. Satellite observations of canopy reflectance are subject to view and illumination effects induced by the bi-directional reflectance distribution function(BRDF) which can confound the desired PRI signal. Further uncertainties include dependencies of PRI on canopy structure, understorey, species composition and leaf pigment concentration. The objective of this research was to investigate the effects of these factors on PRI to facilitate the modeling of GPP in a continuous fashion. Canopy spectra were sampled over a one-year period using an automated tower-based, multi-angular spectroradiometer platform (AMSPEC), designed to sample high spectral resolution data. The wide range of illumination and viewing geometries seen by the instrument permitted comprehensive modeling of the BRDF. Isolation of physiologically induced changes in PRI yielded a high correlation (r²=0.82, p<0.05) to EC-measured ε, thereby demonstrating the capability of PRI to model ε throughout the year. The results were extrapolated to the landscape scale using airborne laser-scanning (light detection and ranging, LiDAR) and high correlations were found between remotely-sensed and EC-measured GPP (r²>0.79, p<0.05). Permanently established tower-based canopy reflectance measurements are helpful for ongoing research aimed at up-scaling ε to landscape and global scales and facilitate a better understanding of physiological cycles of vegetation and serve as a calibration tool for broader band satellite observations. / Forestry, Faculty of / Graduate

<p> Impacts to wetlands protected under Clean Water Act Sections 401 and 404, can require mitigation with the goal of no net loss of acreage and function. Several studies of compensatory mitigation concluded this goal has not been met, resulting in cumulative losses. Many of these studies were completed by permit review or post-mitigation field survey. However, few studies evaluated condition before and after impact and mitigation activities as a comparison of losses and gains to assess net loss of wetlands.</p><p> Ambrose et al. (2007) evaluated both compliance and wetland condition for CWA &sect;401 mitigation projects throughout California. As a continuance of the studies of Ambrose et al. (2007), this thesis evaluated the change in condition as a result of impact and mitigation activities to address whether no net loss was achieved. A rapid assessment methodology was adapted to evaluate wetland condition using aerial photographs.</p><p> As hypothesized, impact activities decreased the wetland condition at the majority of sites. However, mitigation activities often did not increase condition. Therefore, no net loss was not achieved for most projects. Furthermore, this study illustrates the pitfalls in an evaluation of no net loss based solely on the mitigation site condition after the project implementation. The observed wetland condition may have been present at the site prior to mitigation activities. This is a misleading assumption in the assessment of gains from the project. Therefore, the change in wetland condition should be assessed through an initial evaluation of site conditions, as well as part of on-going monitoring.</p><p>